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Results: We propose CMGL, a two-stage framework that estimates per-sample modality reliability through evidential deep learning and uses the frozen confidence scores to guide cross-omics fusion and graph construction. On four MLOmics cancer-subtype tasks and the 32-class pan-cancer task, CMGL consistently improves over the strongest baseline, surpassing it by 4.03% in average accuracy on the four single-cancer tasks. Its representations recover the PAM50 intrinsic subtypes of breast invasive carcinoma (BRCA), and the BRCA-trained model transfers without fine-tuning to kidney renal clear cell carcinoma (KIRC), stratifying patients into prognostically distinct groups.
| Comments: | 24 pages, 15 figures, 13 tables, 2 algorithms (main paper + supplementary materials) |
| Subjects: | Machine Learning (cs.LG); Genomics (q-bio.GN); Molecular Networks (q-bio.MN) |
| MSC classes: | 62H30, 68T07, 92C40 |
| ACM classes: | I.2.6; J.3 |
| Cite as: | arXiv:2604.24201 [cs.LG] |
| (or arXiv:2604.24201v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.24201 arXiv-issued DOI via DataCite (pending registration) |
From: Boyang Fan [view email]
[v1]
Mon, 27 Apr 2026 09:02:50 UTC (4,362 KB)
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